Abstract
Small intestinal neuroendocrine tumors (SI-NETs) remain the most common subset in gastrointestinal neuroendocrine tumors and featured by aggressiveness. However, the molecular feature of SI-NETs remains largely unclear with key genes and pathways yet to be identified. The gene expression profile GSE65286 was retrieved for analysis. Artificial neural networks (ANNs) were constructed for the hub genes network models. A total of 613 differentially expressed genes (DEGs) were identified between normal (N) and primary tumor (T) groups, whereas 61 DEGs were identified between T and liver metastases (LM) groups. The top Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for the DEGs of N versus T were fat digestion and absorption pathway. For T versus LM the top KEGG pathways were complement and coagulation. In gene set enrichment analysis (GSEA), five gene sets, including Notch signaling, inflammatory response, coagulation, KRAS signaling, and allograft rejection were significantly enriched in the T group. The hub genes in the DEGs of T versus LM included albumin, fibrinogen gamma chain, alpha 2-HS glycoprotein, transferrin and GC, vitamin D binding protein. A distinct correlational alteration of hub genes was observed between T and LM groups. In ANN analysis, ALB and TF were the top predictors of metastasis. Moreover, the expression of ALB≤ showed the highest support to T whereas ALB>15.97 supports LM. TF≤7.54 showed the highest negative correlation to the T. This bioinformatics analysis provided insights on potential key pathways and genes networks involved in SI-NETs and established an ANN-based hub gene model for metastatic prediction.
Keywords:
Differentially expressed genes; Gene ontology; Gene set enrichment analysis; KEGG pathway; Protein-protein interaction network; Small intestinal neuroendocrine tumors.